Overview

Dataset statistics

Number of variables14
Number of observations26064
Missing cells16051
Missing cells (%)4.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.8 MiB
Average record size in memory112.0 B

Variable types

DateTime1
Numeric13

Alerts

Power (kW) is highly overall correlated with Rear bearing temperature (°C) and 6 other fieldsHigh correlation
Wind direction (°) is highly overall correlated with Nacelle position (°)High correlation
Nacelle position (°) is highly overall correlated with Wind direction (°)High correlation
Rear bearing temperature (°C) is highly overall correlated with Power (kW) and 5 other fieldsHigh correlation
Rotor speed (RPM) is highly overall correlated with Power (kW) and 6 other fieldsHigh correlation
Generator RPM (RPM) is highly overall correlated with Power (kW) and 6 other fieldsHigh correlation
Nacelle ambient temperature (°C) is highly overall correlated with Metal particle count counterHigh correlation
Front bearing temperature (°C) is highly overall correlated with Power (kW) and 6 other fieldsHigh correlation
Tower Acceleration X (mm/ss) is highly overall correlated with Power (kW) and 5 other fieldsHigh correlation
Wind speed (m/s) is highly overall correlated with Power (kW) and 6 other fieldsHigh correlation
Tower Acceleration y (mm/ss) is highly overall correlated with Power (kW) and 6 other fieldsHigh correlation
Metal particle count counter is highly overall correlated with Nacelle ambient temperature (°C)High correlation
blade_angle has 2245 (8.6%) missing valuesMissing
Rear bearing temperature (°C) has 2245 (8.6%) missing valuesMissing
Nacelle ambient temperature (°C) has 2245 (8.6%) missing valuesMissing
Front bearing temperature (°C) has 2245 (8.6%) missing valuesMissing
Tower Acceleration X (mm/ss) has 2245 (8.6%) missing valuesMissing
Tower Acceleration y (mm/ss) has 2245 (8.6%) missing valuesMissing
Metal particle count counter has 2245 (8.6%) missing valuesMissing
# Date and time has unique valuesUnique
blade_angle has 9929 (38.1%) zerosZeros
Rotor speed (RPM) has 912 (3.5%) zerosZeros

Reproduction

Analysis started2023-07-08 12:01:34.047313
Analysis finished2023-07-08 12:01:50.154882
Duration16.11 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

# Date and time
Date

Distinct26064
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size203.8 KiB
Minimum2021-01-01 00:00:00
Maximum2021-06-30 23:50:00
2023-07-08T17:31:50.202310image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:50.300644image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Power (kW)
Real number (ℝ)

Distinct25874
Distinct (%)99.5%
Missing56
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean657.91262
Minimum-14.919425
Maximum2074.4718
Zeros1
Zeros (%)< 0.1%
Negative3414
Negative (%)13.1%
Memory size203.8 KiB
2023-07-08T17:31:50.521338image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-14.919425
5-th percentile-2.2090368
Q197.269755
median377.82666
Q31083.3908
95-th percentile2036.5655
Maximum2074.4718
Range2089.3912
Interquartile range (IQR)986.12108

Descriptive statistics

Standard deviation684.40243
Coefficient of variation (CV)1.0402634
Kurtosis-0.55782379
Mean657.91262
Median Absolute Deviation (MAD)356.04773
Skewness0.91290938
Sum17110991
Variance468406.69
MonotonicityNot monotonic
2023-07-08T17:31:50.610893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.110000014 8
 
< 0.1%
-1.190000057 8
 
< 0.1%
-1.169999957 7
 
< 0.1%
-1.090000033 7
 
< 0.1%
-1.080000043 5
 
< 0.1%
-1.029999971 4
 
< 0.1%
-0.9399999976 4
 
< 0.1%
-0.8899999857 4
 
< 0.1%
-0.9100000262 4
 
< 0.1%
-1.139999986 4
 
< 0.1%
Other values (25864) 25953
99.6%
(Missing) 56
 
0.2%
ValueCountFrequency (%)
-14.91942476 1
< 0.1%
-14.33299656 1
< 0.1%
-14.29828612 1
< 0.1%
-13.78164353 1
< 0.1%
-13.74103556 1
< 0.1%
-13.095603 1
< 0.1%
-12.6738742 1
< 0.1%
-12.5835827 1
< 0.1%
-12.43853095 1
< 0.1%
-12.31941945 1
< 0.1%
ValueCountFrequency (%)
2074.471771 1
< 0.1%
2073.389734 1
< 0.1%
2072.921313 1
< 0.1%
2072.799121 1
< 0.1%
2072.247491 1
< 0.1%
2070.840552 1
< 0.1%
2070.345416 1
< 0.1%
2069.6625 1
< 0.1%
2069.380811 1
< 0.1%
2069.28797 1
< 0.1%

Wind direction (°)
Real number (ℝ)

Distinct25829
Distinct (%)99.3%
Missing56
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean179.7074
Minimum0.02
Maximum359.92188
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size203.8 KiB
2023-07-08T17:31:50.705276image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.02
5-th percentile20.196173
Q176.762484
median200.31034
Q3264.1001
95-th percentile330.74406
Maximum359.92188
Range359.90188
Interquartile range (IQR)187.33761

Descriptive statistics

Standard deviation103.32819
Coefficient of variation (CV)0.57498015
Kurtosis-1.278273
Mean179.7074
Median Absolute Deviation (MAD)89.395942
Skewness-0.15905084
Sum4673830
Variance10676.714
MonotonicityNot monotonic
2023-07-08T17:31:50.803255image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
44.08000183 4
 
< 0.1%
45.04999924 4
 
< 0.1%
55.06000137 3
 
< 0.1%
40.04999924 3
 
< 0.1%
43.63999939 3
 
< 0.1%
44.45999908 3
 
< 0.1%
23.85000038 3
 
< 0.1%
49.45000076 3
 
< 0.1%
43.86999893 3
 
< 0.1%
44.54000092 3
 
< 0.1%
Other values (25819) 25976
99.7%
(Missing) 56
 
0.2%
ValueCountFrequency (%)
0.01999999955 1
< 0.1%
0.06696112955 1
< 0.1%
0.09737332416 1
< 0.1%
0.1148331045 1
< 0.1%
0.1299999952 1
< 0.1%
0.136807827 1
< 0.1%
0.1385611858 1
< 0.1%
0.1800000072 1
< 0.1%
0.1928611364 1
< 0.1%
0.1951241894 1
< 0.1%
ValueCountFrequency (%)
359.921878 1
< 0.1%
359.8934008 1
< 0.1%
359.8903118 1
< 0.1%
359.8599854 2
< 0.1%
359.8398508 1
< 0.1%
359.7846462 1
< 0.1%
359.7829887 1
< 0.1%
359.7738512 1
< 0.1%
359.7382805 1
< 0.1%
359.7300255 1
< 0.1%

Nacelle position (°)
Real number (ℝ)

Distinct7084
Distinct (%)27.2%
Missing56
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean179.28902
Minimum0.22697348
Maximum359.90556
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size203.8 KiB
2023-07-08T17:31:50.905042image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.22697348
5-th percentile21.09
Q174.875328
median201.0943
Q3263.30805
95-th percentile330.60699
Maximum359.90556
Range359.67859
Interquartile range (IQR)188.43272

Descriptive statistics

Standard deviation103.6666
Coefficient of variation (CV)0.57820941
Kurtosis-1.2896073
Mean179.28902
Median Absolute Deviation (MAD)90.521213
Skewness-0.15860962
Sum4662948.8
Variance10746.764
MonotonicityNot monotonic
2023-07-08T17:31:51.001753image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
262.5580139 102
 
0.4%
103.4112778 102
 
0.4%
303.1678772 99
 
0.4%
197.8016663 92
 
0.4%
206.5821838 92
 
0.4%
206.5822144 89
 
0.3%
44.14312363 86
 
0.3%
45.24068069 85
 
0.3%
11.2168417 83
 
0.3%
16.70000076 81
 
0.3%
Other values (7074) 25097
96.3%
ValueCountFrequency (%)
0.2269734777 1
 
< 0.1%
0.2399999946 9
< 0.1%
0.2406311035 2
 
< 0.1%
0.2406413555 2
 
< 0.1%
0.2406602599 1
 
< 0.1%
0.240661636 1
 
< 0.1%
0.240753144 3
 
< 0.1%
0.2411193848 13
< 0.1%
0.2411754131 12
< 0.1%
0.2412364632 14
0.1%
ValueCountFrequency (%)
359.9055615 1
 
< 0.1%
359.8031958 1
 
< 0.1%
359.7829159 1
 
< 0.1%
359.75809 1
 
< 0.1%
359.565751 1
 
< 0.1%
359.4838162 1
 
< 0.1%
359.1910123 1
 
< 0.1%
359.1436768 10
< 0.1%
359.1435852 4
 
< 0.1%
359.1435547 1
 
< 0.1%

blade_angle
Real number (ℝ)

MISSING  ZEROS 

Distinct8900
Distinct (%)37.4%
Missing2245
Missing (%)8.6%
Infinite0
Infinite (%)0.0%
Mean7.8048173
Minimum-169.85333
Maximum92.716667
Zeros9929
Zeros (%)38.1%
Negative6
Negative (%)< 0.1%
Memory size203.8 KiB
2023-07-08T17:31:51.103895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-169.85333
5-th percentile0
Q10
median0.11166667
Q33.6323333
95-th percentile44.990002
Maximum92.716667
Range262.57
Interquartile range (IQR)3.6323333

Descriptive statistics

Standard deviation18.21532
Coefficient of variation (CV)2.333856
Kurtosis9.2867588
Mean7.8048173
Median Absolute Deviation (MAD)0.11166667
Skewness2.5265671
Sum185902.94
Variance331.79787
MonotonicityNot monotonic
2023-07-08T17:31:51.204412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9929
38.1%
44.99000168 2152
 
8.3%
89.98999786 396
 
1.5%
0.02450000048 326
 
1.3%
1.49000001 246
 
0.9%
0.04900000095 148
 
0.6%
0.07350000143 78
 
0.3%
0.4900000095 56
 
0.2%
0.09800000191 53
 
0.2%
0.07400000095 37
 
0.1%
Other values (8890) 10398
39.9%
(Missing) 2245
 
8.6%
ValueCountFrequency (%)
-169.8533325 4
 
< 0.1%
-165.4816659 1
 
< 0.1%
-21.19166743 1
 
< 0.1%
0 9929
38.1%
0.0001666666629 12
 
< 0.1%
0.0001754385926 1
 
< 0.1%
0.000185185181 1
 
< 0.1%
0.0003333333259 5
 
< 0.1%
0.000392156854 1
 
< 0.1%
0.000392156854 1
 
< 0.1%
ValueCountFrequency (%)
92.71666718 2
 
< 0.1%
92.48999786 26
0.1%
92.38299904 1
 
< 0.1%
92.15666453 2
 
< 0.1%
92.14333598 10
 
< 0.1%
92.13999939 3
 
< 0.1%
92.13933411 1
 
< 0.1%
92.13666789 5
 
< 0.1%
92.1333313 4
 
< 0.1%
92.13000234 9
 
< 0.1%

Rear bearing temperature (°C)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct19592
Distinct (%)82.3%
Missing2245
Missing (%)8.6%
Infinite0
Infinite (%)0.0%
Mean62.920383
Minimum10.245
Maximum75.0925
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size203.8 KiB
2023-07-08T17:31:51.297698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum10.245
5-th percentile35.26975
Q160.7325
median67.877501
Q370.142498
95-th percentile72.250251
Maximum75.0925
Range64.8475
Interquartile range (IQR)9.4099983

Descriptive statistics

Standard deviation11.848702
Coefficient of variation (CV)0.18831262
Kurtosis3.942609
Mean62.920383
Median Absolute Deviation (MAD)3.1099998
Skewness-2.0305253
Sum1498700.6
Variance140.39174
MonotonicityNot monotonic
2023-07-08T17:31:51.392951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69.41250038 7
 
< 0.1%
70.525 7
 
< 0.1%
69.42999992 7
 
< 0.1%
71.04750023 6
 
< 0.1%
69.79249992 6
 
< 0.1%
70 6
 
< 0.1%
69.40999985 6
 
< 0.1%
70.37250023 6
 
< 0.1%
69.72249985 6
 
< 0.1%
69.54999962 6
 
< 0.1%
Other values (19582) 23756
91.1%
(Missing) 2245
 
8.6%
ValueCountFrequency (%)
10.24499998 1
< 0.1%
10.24499998 1
< 0.1%
10.26250005 1
< 0.1%
10.2750001 1
< 0.1%
10.30000019 1
< 0.1%
10.34249997 1
< 0.1%
10.34500003 1
< 0.1%
10.34999998 1
< 0.1%
10.38499975 1
< 0.1%
10.39999962 1
< 0.1%
ValueCountFrequency (%)
75.09249954 1
< 0.1%
74.65 1
< 0.1%
74.31500015 1
< 0.1%
74.23124981 1
< 0.1%
74.20999985 1
< 0.1%
74.09736834 1
< 0.1%
74.02999878 1
< 0.1%
73.97105247 1
< 0.1%
73.95249939 1
< 0.1%
73.94999886 1
< 0.1%

Rotor speed (RPM)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23657
Distinct (%)91.0%
Missing56
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean10.212647
Minimum0
Maximum15.321547
Zeros912
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size203.8 KiB
2023-07-08T17:31:51.496402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.23820234
Q18.1753366
median10.592774
Q314.396701
95-th percentile15.166954
Maximum15.321547
Range15.321547
Interquartile range (IQR)6.2213644

Descriptive statistics

Standard deviation4.5017993
Coefficient of variation (CV)0.4408063
Kurtosis0.095491308
Mean10.212647
Median Absolute Deviation (MAD)2.4539689
Skewness-0.91628236
Sum265610.53
Variance20.266197
MonotonicityNot monotonic
2023-07-08T17:31:51.592945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 912
 
3.5%
8.140000343 212
 
0.8%
8.149999619 41
 
0.2%
8.159999847 17
 
0.1%
15.14999962 15
 
0.1%
8.18999958 14
 
0.1%
15.13000011 14
 
0.1%
8.170000076 14
 
0.1%
8.199999809 13
 
< 0.1%
8.180000305 11
 
< 0.1%
Other values (23647) 24745
94.9%
(Missing) 56
 
0.2%
ValueCountFrequency (%)
0 912
3.5%
0.004576000851 1
 
< 0.1%
0.009999999776 1
 
< 0.1%
0.01050000242 8
 
< 0.1%
0.01073500235 1
 
< 0.1%
0.0110000018 9
 
< 0.1%
0.01129700197 1
 
< 0.1%
0.01150000188 4
 
< 0.1%
0.01157894927 1
 
< 0.1%
0.01200000197 5
 
< 0.1%
ValueCountFrequency (%)
15.32154661 1
< 0.1%
15.30665816 1
< 0.1%
15.29358584 1
< 0.1%
15.28360954 1
< 0.1%
15.28220661 1
< 0.1%
15.27849296 1
< 0.1%
15.27043019 1
< 0.1%
15.26888359 1
< 0.1%
15.2688594 1
< 0.1%
15.2680487 1
< 0.1%

Generator RPM (RPM)
Real number (ℝ)

Distinct25815
Distinct (%)99.3%
Missing56
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean1213.1494
Minimum-38.021271
Maximum1815.6987
Zeros12
Zeros (%)< 0.1%
Negative1
Negative (%)< 0.1%
Memory size203.8 KiB
2023-07-08T17:31:51.696108image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-38.021271
5-th percentile32.257488
Q1972.78831
median1258.7176
Q31708.2798
95-th percentile1798.8439
Maximum1815.6987
Range1853.72
Interquartile range (IQR)735.49151

Descriptive statistics

Standard deviation533.02716
Coefficient of variation (CV)0.4393747
Kurtosis0.10444146
Mean1213.1494
Median Absolute Deviation (MAD)290.33138
Skewness-0.92155199
Sum31551590
Variance284117.96
MonotonicityNot monotonic
2023-07-08T17:31:51.791696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 12
 
< 0.1%
970.0200195 11
 
< 0.1%
970 11
 
< 0.1%
970.0300293 11
 
< 0.1%
970.0499878 9
 
< 0.1%
970.0100098 8
 
< 0.1%
969.9299927 8
 
< 0.1%
970.0800171 7
 
< 0.1%
969.9699707 7
 
< 0.1%
970.0599976 7
 
< 0.1%
Other values (25805) 25917
99.4%
(Missing) 56
 
0.2%
ValueCountFrequency (%)
-38.02127131 1
 
< 0.1%
0 12
< 0.1%
0.1099999994 1
 
< 0.1%
0.1700000018 1
 
< 0.1%
0.3000000119 1
 
< 0.1%
0.400000006 1
 
< 0.1%
1.120000005 1
 
< 0.1%
1.375096712 1
 
< 0.1%
1.517979043 1
 
< 0.1%
1.536487 1
 
< 0.1%
ValueCountFrequency (%)
1815.69873 1
< 0.1%
1814.782076 1
< 0.1%
1813.813637 1
< 0.1%
1812.747631 1
< 0.1%
1811.651505 1
< 0.1%
1811.641062 1
< 0.1%
1811.600224 1
< 0.1%
1811.407356 1
< 0.1%
1810.44401 1
< 0.1%
1810.394703 1
< 0.1%

Nacelle ambient temperature (°C)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct18537
Distinct (%)77.8%
Missing2245
Missing (%)8.6%
Infinite0
Infinite (%)0.0%
Mean8.3213288
Minimum-3.3975001
Maximum27.295
Zeros0
Zeros (%)0.0%
Negative972
Negative (%)3.7%
Memory size203.8 KiB
2023-07-08T17:31:51.892296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-3.3975001
5-th percentile0.26000001
Q14.4000001
median7.8450002
Q311.255
95-th percentile19.4255
Maximum27.295
Range30.6925
Interquartile range (IQR)6.8550001

Descriptive statistics

Standard deviation5.5562825
Coefficient of variation (CV)0.66771578
Kurtosis0.13131921
Mean8.3213288
Median Absolute Deviation (MAD)3.4249999
Skewness0.59173022
Sum198205.73
Variance30.872275
MonotonicityNot monotonic
2023-07-08T17:31:51.982323image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.599999905 37
 
0.1%
4.5 32
 
0.1%
6.699999809 29
 
0.1%
7.900000095 29
 
0.1%
6.300000191 27
 
0.1%
1.399999976 25
 
0.1%
8.5 25
 
0.1%
6.400000095 25
 
0.1%
7.5 24
 
0.1%
3.700000048 24
 
0.1%
Other values (18527) 23542
90.3%
(Missing) 2245
 
8.6%
ValueCountFrequency (%)
-3.397500086 1
< 0.1%
-3.387500072 1
< 0.1%
-3.367499995 1
< 0.1%
-3.365789514 1
< 0.1%
-3.332499957 1
< 0.1%
-3.318420987 1
< 0.1%
-3.299999952 1
< 0.1%
-3.289999938 1
< 0.1%
-3.224999928 1
< 0.1%
-3.204999876 1
< 0.1%
ValueCountFrequency (%)
27.29500027 1
< 0.1%
27.25750008 1
< 0.1%
27.05000019 1
< 0.1%
27.04749994 1
< 0.1%
26.81499987 1
< 0.1%
26.76500015 1
< 0.1%
26.6625 1
< 0.1%
26.62750034 1
< 0.1%
26.56000004 1
< 0.1%
26.48500032 1
< 0.1%

Front bearing temperature (°C)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct19594
Distinct (%)82.3%
Missing2245
Missing (%)8.6%
Infinite0
Infinite (%)0.0%
Mean63.474783
Minimum10.3
Maximum76.414999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size203.8 KiB
2023-07-08T17:31:52.080118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum10.3
5-th percentile35.499501
Q158.7625
median69.735
Q372.0325
95-th percentile73.7475
Maximum76.414999
Range66.114999
Interquartile range (IQR)13.27

Descriptive statistics

Standard deviation12.724978
Coefficient of variation (CV)0.20047297
Kurtosis2.4370218
Mean63.474783
Median Absolute Deviation (MAD)3.3824989
Skewness-1.689598
Sum1511905.8
Variance161.92508
MonotonicityNot monotonic
2023-07-08T17:31:52.171489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72.25250015 8
 
< 0.1%
72.27750168 7
 
< 0.1%
72.24499969 7
 
< 0.1%
70.48499947 7
 
< 0.1%
72.27500153 7
 
< 0.1%
72.07500038 7
 
< 0.1%
70.47749977 7
 
< 0.1%
70.575 7
 
< 0.1%
72.24249954 7
 
< 0.1%
70.49249954 7
 
< 0.1%
Other values (19584) 23748
91.1%
(Missing) 2245
 
8.6%
ValueCountFrequency (%)
10.30000019 3
< 0.1%
10.30500016 1
 
< 0.1%
10.31250014 1
 
< 0.1%
10.39999962 3
< 0.1%
10.46499987 1
 
< 0.1%
10.46999969 1
 
< 0.1%
10.5 1
 
< 0.1%
10.51999998 1
 
< 0.1%
10.60000038 4
< 0.1%
10.62000027 1
 
< 0.1%
ValueCountFrequency (%)
76.41499939 1
< 0.1%
76.28500175 1
< 0.1%
76.21250114 1
< 0.1%
76.18750076 1
< 0.1%
76.18500099 1
< 0.1%
76.12000084 1
< 0.1%
76.09000092 1
< 0.1%
76.06749992 1
< 0.1%
76.06500015 1
< 0.1%
76.05500069 1
< 0.1%

Tower Acceleration X (mm/ss)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct23819
Distinct (%)100.0%
Missing2245
Missing (%)8.6%
Infinite0
Infinite (%)0.0%
Mean47.127509
Minimum2.5655193
Maximum231.75502
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size203.8 KiB
2023-07-08T17:31:52.268287image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2.5655193
5-th percentile4.4554881
Q126.63293
median43.95299
Q363.514058
95-th percentile102.14448
Maximum231.75502
Range229.1895
Interquartile range (IQR)36.881128

Descriptive statistics

Standard deviation29.496196
Coefficient of variation (CV)0.62588065
Kurtosis0.75239822
Mean47.127509
Median Absolute Deviation (MAD)18.440873
Skewness0.73852763
Sum1122530.1
Variance870.02558
MonotonicityNot monotonic
2023-07-08T17:31:52.363384image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.242331445 1
 
< 0.1%
48.84061775 1
 
< 0.1%
37.38439903 1
 
< 0.1%
73.32390966 1
 
< 0.1%
52.28655086 1
 
< 0.1%
53.56809301 1
 
< 0.1%
38.32797181 1
 
< 0.1%
37.0401021 1
 
< 0.1%
69.95642395 1
 
< 0.1%
64.84148231 1
 
< 0.1%
Other values (23809) 23809
91.3%
(Missing) 2245
 
8.6%
ValueCountFrequency (%)
2.565519291 1
< 0.1%
2.996320635 1
< 0.1%
3.038561606 1
< 0.1%
3.041536754 1
< 0.1%
3.067302281 1
< 0.1%
3.101516068 1
< 0.1%
3.205448413 1
< 0.1%
3.205981654 1
< 0.1%
3.226414388 1
< 0.1%
3.270928806 1
< 0.1%
ValueCountFrequency (%)
231.7550224 1
< 0.1%
226.5720161 1
< 0.1%
210.4599203 1
< 0.1%
210.0537077 1
< 0.1%
206.6951744 1
< 0.1%
205.4213232 1
< 0.1%
203.6151227 1
< 0.1%
203.4742215 1
< 0.1%
196.6631751 1
< 0.1%
194.2196182 1
< 0.1%

Wind speed (m/s)
Real number (ℝ)

Distinct24566
Distinct (%)94.5%
Missing56
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean6.0472709
Minimum0.21380643
Maximum20.891098
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size203.8 KiB
2023-07-08T17:31:52.581376image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.21380643
5-th percentile1.9265926
Q13.8361191
median5.6400555
Q37.8498931
95-th percentile11.72952
Maximum20.891098
Range20.677292
Interquartile range (IQR)4.013774

Descriptive statistics

Standard deviation2.9853368
Coefficient of variation (CV)0.4936668
Kurtosis0.21117288
Mean6.0472709
Median Absolute Deviation (MAD)1.9800554
Skewness0.66558035
Sum157277.42
Variance8.912236
MonotonicityNot monotonic
2023-07-08T17:31:52.674569image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.599999905 10
 
< 0.1%
4.579999924 10
 
< 0.1%
4.769999981 9
 
< 0.1%
3.900000095 9
 
< 0.1%
4.519999981 9
 
< 0.1%
4.449999809 9
 
< 0.1%
5.309999943 9
 
< 0.1%
5.659999847 8
 
< 0.1%
3.619999886 8
 
< 0.1%
4.860000134 8
 
< 0.1%
Other values (24556) 25919
99.4%
(Missing) 56
 
0.2%
ValueCountFrequency (%)
0.2138064291 1
< 0.1%
0.2190378051 1
< 0.1%
0.2305315316 1
< 0.1%
0.2877751738 1
< 0.1%
0.2892376455 1
< 0.1%
0.2959875982 1
< 0.1%
0.3044814784 1
< 0.1%
0.3068814054 1
< 0.1%
0.3071627364 1
< 0.1%
0.3121500971 1
< 0.1%
ValueCountFrequency (%)
20.8910984 1
< 0.1%
20.60519876 1
< 0.1%
20.31605086 1
< 0.1%
19.87541418 1
< 0.1%
19.86305141 1
< 0.1%
19.86166392 1
< 0.1%
19.59283719 1
< 0.1%
19.54589252 1
< 0.1%
19.50965848 1
< 0.1%
19.48573151 1
< 0.1%

Tower Acceleration y (mm/ss)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct23819
Distinct (%)100.0%
Missing2245
Missing (%)8.6%
Infinite0
Infinite (%)0.0%
Mean27.406254
Minimum2.8739483
Maximum202.38986
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size203.8 KiB
2023-07-08T17:31:52.771724image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2.8739483
5-th percentile4.3135843
Q114.783932
median23.268776
Q335.700319
95-th percentile64.231615
Maximum202.38986
Range199.51591
Interquartile range (IQR)20.916386

Descriptive statistics

Standard deviation18.72773
Coefficient of variation (CV)0.68333785
Kurtosis4.2725651
Mean27.406254
Median Absolute Deviation (MAD)10.072677
Skewness1.5390302
Sum652789.56
Variance350.72789
MonotonicityNot monotonic
2023-07-08T17:31:52.871671image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.544389236 1
 
< 0.1%
25.3180053 1
 
< 0.1%
24.90042284 1
 
< 0.1%
35.74887569 1
 
< 0.1%
32.3395973 1
 
< 0.1%
35.13156843 1
 
< 0.1%
25.39304465 1
 
< 0.1%
28.96124816 1
 
< 0.1%
23.36700711 1
 
< 0.1%
26.11527753 1
 
< 0.1%
Other values (23809) 23809
91.3%
(Missing) 2245
 
8.6%
ValueCountFrequency (%)
2.87394833 1
< 0.1%
3.124783689 1
< 0.1%
3.130565554 1
< 0.1%
3.168063143 1
< 0.1%
3.176988488 1
< 0.1%
3.193014771 1
< 0.1%
3.207721865 1
< 0.1%
3.216927877 1
< 0.1%
3.24272148 1
< 0.1%
3.258354872 1
< 0.1%
ValueCountFrequency (%)
202.3898567 1
< 0.1%
185.463901 1
< 0.1%
181.1243958 1
< 0.1%
174.2728586 1
< 0.1%
171.5338733 1
< 0.1%
167.1090007 1
< 0.1%
165.6609005 1
< 0.1%
165.3844678 1
< 0.1%
164.7697454 1
< 0.1%
162.362461 1
< 0.1%

Metal particle count counter
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct10
Distinct (%)< 0.1%
Missing2245
Missing (%)8.6%
Infinite0
Infinite (%)0.0%
Mean546.89559
Minimum541
Maximum550
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size203.8 KiB
2023-07-08T17:31:52.961264image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum541
5-th percentile544
Q1545
median547
Q3550
95-th percentile550
Maximum550
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.2644404
Coefficient of variation (CV)0.0041405351
Kurtosis-1.231366
Mean546.89559
Median Absolute Deviation (MAD)2
Skewness0.090498938
Sum13026506
Variance5.1276902
MonotonicityIncreasing
2023-07-08T17:31:53.030669image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
550 6225
23.9%
545 5384
20.7%
547 4949
19.0%
544 3586
13.8%
548 2719
10.4%
546 462
 
1.8%
543 342
 
1.3%
541 101
 
0.4%
549 48
 
0.2%
542 3
 
< 0.1%
(Missing) 2245
 
8.6%
ValueCountFrequency (%)
541 101
 
0.4%
542 3
 
< 0.1%
543 342
 
1.3%
544 3586
13.8%
545 5384
20.7%
546 462
 
1.8%
547 4949
19.0%
548 2719
10.4%
549 48
 
0.2%
550 6225
23.9%
ValueCountFrequency (%)
550 6225
23.9%
549 48
 
0.2%
548 2719
10.4%
547 4949
19.0%
546 462
 
1.8%
545 5384
20.7%
544 3586
13.8%
543 342
 
1.3%
542 3
 
< 0.1%
541 101
 
0.4%

Interactions

2023-07-08T17:31:48.503647image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:34.708883image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:35.882122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:37.012393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:38.137067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:39.318858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:40.430017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:41.577593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:42.715240image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:43.931602image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:45.053212image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:46.148859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:47.343978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:48.581844image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:34.782239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:35.963050image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:37.091926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:38.211795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:39.397704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:40.511571image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:41.659349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:42.793382image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:44.013218image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:45.131198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:46.226170image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:47.426479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:48.666722image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:34.963495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:36.051707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:37.182553image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:38.297435image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:39.488263image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:40.604644image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:41.749181image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:42.996653image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:44.102502image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:45.217993image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:46.312367image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:47.519956image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:48.753595image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:35.047188image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:36.140541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:37.269366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:38.381867image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:39.576810image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:40.694487image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:41.840474image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:43.084361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:44.191784image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:45.303178image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:46.399429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:47.612729image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:48.833271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:35.126120image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:36.225471image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:37.354540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:38.461863image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:39.660856image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:40.782225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:41.927591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:43.165904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:44.276384image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:45.386081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:46.480846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:47.698805image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:48.918608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:35.209694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:36.314426image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:37.448372image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:38.545308image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:39.745312image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:40.870318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:42.015465image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:43.252671image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:44.363840image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:45.470248image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:46.564558image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:47.793603image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:49.007024image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:35.302405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:36.408018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:37.547303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:38.633889image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:39.836729image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:40.963783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:42.109416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:43.344265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:44.456792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:45.563668image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:46.655519image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:47.888243image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:49.091995image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:35.389754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:36.497293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:37.634425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:38.718710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:39.924955image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:41.053345image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:42.196925image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:43.431494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:44.544706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:45.651267image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:46.742162image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:47.981324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:49.174470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:35.473144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:36.583252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:37.718751image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:38.798856image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:40.009150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:41.140733image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:42.282976image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:43.514019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:44.629160image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:45.732290image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:46.824019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:48.069020image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:49.258097image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:35.555706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:36.670172image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:37.804318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:38.997184image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:40.094988image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:41.230702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:42.372174image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:43.599687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:44.713669image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:45.823193image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:46.908168image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:48.161542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:49.338584image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:35.632960image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:36.752778image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:37.884863image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:39.072935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:40.174781image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:41.315139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:42.455954image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:43.680437image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:44.795487image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:45.899482image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:46.988171image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:48.243583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:49.415703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:35.709382image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:36.835694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:37.964966image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:39.150616image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:40.255689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:41.396996image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:42.537436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:43.758743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:44.876032image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:45.979580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:47.175453image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:48.325105image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:49.506068image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:35.802375image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:36.928470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:38.056562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:39.238569image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:40.347100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:41.492037image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:42.632318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:43.850912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:44.969433image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:46.067581image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:47.264277image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:48.419731image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-07-08T17:31:53.104537image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Power (kW)Wind direction (°)Nacelle position (°)blade_angleRear bearing temperature (°C)Rotor speed (RPM)Generator RPM (RPM)Nacelle ambient temperature (°C)Front bearing temperature (°C)Tower Acceleration X (mm/ss)Wind speed (m/s)Tower Acceleration y (mm/ss)Metal particle count counter
Power (kW)1.0000.0740.073-0.2200.7770.9910.991-0.1090.8710.6500.9660.837-0.182
Wind direction (°)0.0741.0000.894-0.0720.1270.0750.0730.1160.121-0.0660.1040.0050.056
Nacelle position (°)0.0730.8941.000-0.0710.1230.0740.0710.1140.119-0.0670.1020.0030.047
blade_angle-0.220-0.072-0.0711.000-0.490-0.233-0.2340.125-0.406-0.034-0.171-0.0570.007
Rear bearing temperature (°C)0.7770.1270.123-0.4901.0000.7740.7710.0060.9080.4480.7420.593-0.042
Rotor speed (RPM)0.9910.0750.074-0.2330.7741.0000.999-0.1120.8720.6490.9620.836-0.185
Generator RPM (RPM)0.9910.0730.071-0.2340.7710.9991.000-0.1230.8710.6480.9620.836-0.190
Nacelle ambient temperature (°C)-0.1090.1160.1140.1250.006-0.112-0.1231.000-0.107-0.025-0.142-0.0870.663
Front bearing temperature (°C)0.8710.1210.119-0.4060.9080.8720.871-0.1071.0000.5120.8370.696-0.135
Tower Acceleration X (mm/ss)0.650-0.066-0.067-0.0340.4480.6490.648-0.0250.5121.0000.6030.867-0.084
Wind speed (m/s)0.9660.1040.102-0.1710.7420.9620.962-0.1420.8370.6031.0000.815-0.223
Tower Acceleration y (mm/ss)0.8370.0050.003-0.0570.5930.8360.836-0.0870.6960.8670.8151.000-0.157
Metal particle count counter-0.1820.0560.0470.007-0.042-0.185-0.1900.663-0.135-0.084-0.223-0.1571.000

Missing values

2023-07-08T17:31:49.623159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-08T17:31:49.808078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-07-08T17:31:50.009027image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

# Date and timePower (kW)Wind direction (°)Nacelle position (°)blade_angleRear bearing temperature (°C)Rotor speed (RPM)Generator RPM (RPM)Nacelle ambient temperature (°C)Front bearing temperature (°C)Tower Acceleration X (mm/ss)Wind speed (m/s)Tower Acceleration y (mm/ss)Metal particle count counter
02021-01-01 00:00:00-3.501361305.089517314.14303689.98999816.0000000.01.6925200.975018.2150015.2423316.3791815.544389541.0
12021-01-01 00:10:00-4.569428301.161486314.14303689.98999815.8100000.02.0934160.550018.1575016.5644026.4780508.374981541.0
22021-01-01 00:20:00-3.048953300.793292314.14303689.98999815.8550000.02.250503-0.155018.0400005.6302465.9339628.232877541.0
32021-01-01 00:30:00-3.463482300.936212314.14303689.98999815.7000000.02.323701-0.460017.8549995.8277545.5130259.733483541.0
42021-01-01 00:40:00-3.873210294.966428314.14303689.98999815.5600000.01.693420-0.410017.6700009.5151495.4307129.843478541.0
52021-01-01 00:50:00-3.766510290.301056314.14303689.98999815.3825000.01.699239-0.240017.4725009.3289805.5709639.515935541.0
62021-01-01 01:00:00-3.903442293.523459314.14303689.98999815.2222220.01.666957-0.075017.2666675.5282015.8660427.951214541.0
72021-01-01 01:10:00-3.964972298.707236314.14303689.98999815.0275000.01.685143-0.027517.1050005.1785915.8590387.219179541.0
82021-01-01 01:20:00-3.668880295.479077314.14303689.98999814.9750000.01.802207-0.200016.93000010.2952545.47980011.514969541.0
92021-01-01 01:30:00-3.391460302.970046314.14303689.98999814.8200000.01.795335-0.077516.8274997.9851855.94736913.127142541.0
# Date and timePower (kW)Wind direction (°)Nacelle position (°)blade_angleRear bearing temperature (°C)Rotor speed (RPM)Generator RPM (RPM)Nacelle ambient temperature (°C)Front bearing temperature (°C)Tower Acceleration X (mm/ss)Wind speed (m/s)Tower Acceleration y (mm/ss)Metal particle count counter
260542021-06-30 22:20:0033.88000136.11000137.560001NaNNaN8.14970.049988NaNNaNNaN3.13NaNNaN
260552021-06-30 22:30:0020.30999935.11000137.560001NaNNaN8.14969.929993NaNNaNNaN2.87NaNNaN
260562021-06-30 22:40:0013.79000034.24000237.560001NaNNaN8.14970.049988NaNNaNNaN2.64NaNNaN
260572021-06-30 22:50:0027.45999938.81000137.560001NaNNaN8.14970.140015NaNNaNNaN3.02NaNNaN
260582021-06-30 23:00:0082.68000039.56000137.560001NaNNaN8.14970.229980NaNNaNNaN3.56NaNNaN
260592021-06-30 23:10:00113.80999842.73000037.560001NaNNaN8.14970.030029NaNNaNNaN3.85NaNNaN
260602021-06-30 23:20:00128.86000143.59000037.560001NaNNaN8.14970.369995NaNNaNNaN4.06NaNNaN
260612021-06-30 23:30:00112.22000140.00000037.560001NaNNaN8.14970.000000NaNNaNNaN4.00NaNNaN
260622021-06-30 23:40:0070.58999634.11000137.560001NaNNaN8.14969.849976NaNNaNNaN3.59NaNNaN
260632021-06-30 23:50:0056.11999936.38999937.560001NaNNaN8.14970.090027NaNNaNNaN3.42NaNNaN